Inertial tracking with automatic drift correction has been demonstrated to be a successful technique for tracking objects, such as limbs, cameras, input devices, or head mounted displays (HMDs), offering low jitter, fast response, increased range, and reduced problems due to interference or line-of-sight occlusion. Inertial trackers have been successfully applied to a wide range of HMD applications including virtual environment (VE) training, virtual prototyping, interactive visualization and design, VR gaming, and even fixed-base vehicle simulation. Within this gamut of applications, inertial trackers have gained widespread acceptance as a high-performance, robust and cost-effective alternatives to magnetic, optical and acoustic tracking systems. InterSense of Burlington, Mass., has pioneered the commercial development of motion tracking systems using miniature MEMS-based inertial sensors, and now offers a broad product line of inertial hybrid trackers .
Until now, inertial trackers have not been used in applications that require tracking motion relative to a moving platform instead of relative to the earth. This includes such important applications as motion-base driving and flight simulators, conventional VE systems deployed on board ships, and a range of live vehicular applications such as driver's or pilot's vision enhancement, helmet-mounted cueing systems, and advanced human-machine interfaces to improve pilots' situational awareness and control capability. People wishing to use inertial trackers in these types of applications have been realized that standard inertial tracking systems such as the InterSense IS-300, 600 or 900 will not function correctly if operated on a moving platform such as a motion-base simulator or vehicle. The inertial sensors would measure head motion relative to the ground, while the drift-correcting range sensors would measure head pose relative to the vehicle platform in which the reference receivers are mounted. While the vehicle is turning or accelerating, the Kalman filter would attempt to fuse inconsistent data and produce unpredictable results.